Applied Data Analytics, Visualization, and Advanced Dashboarding with Power BI (Phase 1: Applied to Advanced Level)

Course Description

Duration 4 Days – 28 hrs.

Overview

This Phase 1 course is a practical, hands-on program that builds core Power BI development skills and grows them into advanced dashboarding capability. It is designed for participants who already work with spreadsheets and reports and are ready to move into Power BI as their primary analysis and reporting tool.

The course follows a deliberate progression across four intensive days: core Power BI skills (connecting to data, shaping it in Power Query, modeling it, and writing foundational DAX), then storytelling, communication, and visualization craft, then advanced dashboarding techniques such as advanced DAX, performance optimization, and row-level security, and finally the basic SQL skills needed to query and understand source systems directly.

Participants will work hands-on in Power BI Desktop throughout the course, using realistic business datasets to build a working data model, a library of DAX measures, and a multi-page dashboard. The course closes with an applied capstone project where participants build and present a complete, story-driven dashboard to a simulated management audience.

Objectives

Connect to common data sources and shape data using Power Query.
Build clean, well-structured data models based on star-schema principles.
Distinguish calculated columns, measures, and calculated tables, and write foundational DAX.
Apply storytelling principles to structure a dashboard narrative around a clear message.
Select the chart type and visual format that best match a business question and audience.
Apply visual design and formatting best practices to reduce clutter and highlight what matters.
Use interactivity – slicers, bookmarks, buttons, and tooltips – to guide viewers through a dashboard.
Write advanced DAX, including time intelligence, iterators, and variables.
Apply advanced modeling techniques, including role-playing dimensions, bridge tables, and row-level security.
Identify and resolve dashboard performance issues, and publish, share, and govern Power BI reports.
Write foundational SQL queries – SELECT, WHERE, JOIN, GROUP BY – against a relational database.
Relate Power BI’s Get Data and query-folding behavior to the SQL running behind the scenes.
Present dashboards and analytical findings clearly to both technical and non-technical stakeholders.
Complete an applied capstone project integrating data modeling, DAX, visualization, and storytelling.

Target Audience

Accounting, finance, and operations professionals who build or maintain recurring dashboards and KPI reports.
Participants who have completed a foundation-level data analysis course, or have equivalent spreadsheet and reporting experience.
Anyone responsible for presenting data-driven insights to management or non-technical audiences.
Power BI developers and BI analysts who want to strengthen core and advanced dashboarding skills.
Reporting and data analysts who currently build reports in Excel and are transitioning to Power BI.

Prerequisites

Basic familiarity with Microsoft Excel, including formulas, tables, and simple PivotTables.
A laptop with Power BI Desktop installed (latest version recommended).
No prior DAX or SQL experience is required; both are introduced from the ground up.
A sample or anonymized organizational dataset is helpful for the capstone project, subject to company confidentiality requirements.

Course Outline

Day 1 – Core Skills Foundations

Module 1: Power BI Ecosystem Overview

Power BI Desktop vs. Power BI Service vs. Power BI Mobile.
Understanding the data flow: Get Data, Transform, Model, Visualize, Publish.
Licensing basics (Free, Pro, Premium) – enough to explain sharing later in the course.

Module 2: Connecting to Data

Common data sources: Excel, CSV, SQL Server, Web, Folder.
Import vs. DirectQuery vs. Live Connection, and when to use each.
Query folding basics and why it matters for performance.

Module 3: Power Query – Shaping and Cleaning Data

The Power Query Editor interface and Applied Steps.
Cleaning data: removing duplicates, changing data types, splitting and merging columns.
Shaping data: unpivot and pivot, group by, merge and append queries.
Best practice: transform in Power Query rather than in DAX when possible.

Module 4: Data Modeling Basics

Star schema vs. snowflake schema.
Fact tables vs. dimension tables.
Relationships: cardinality and cross-filter direction.
Building a clean model from raw tables.

Module 5: Introduction to DAX

Calculated columns vs. measures vs. calculated tables.
Core functions: SUM, AVERAGE, COUNTROWS, DISTINCTCOUNT.
Row context vs. filter context, introduced conceptually.
CALCULATE as the gateway function.

Day 1 workshop: Import a raw business dataset, clean it in Power Query, build a star-schema model, and create five basic DAX measures.

Day 2 – Storytelling, Communication, and Visualization Tools

Module 6: Data Storytelling Principles

The narrative arc: context, insight, so-what, and call to action.
Identifying the audience – executive, operational, or analyst – and adjusting depth accordingly.
One insight per visual, and avoiding chart clutter.

Module 7: Choosing the Right Visual

Matching the visual to the question: comparison, trend, composition, relationship, distribution.
When not to use pie charts, gauges, or 3D visuals.
Table vs. matrix vs. card – when each communicates better than a chart.

Module 8: Visual Design and Formatting

Color theory for dashboards: sequential, diverging, and categorical palettes.
Typography and visual hierarchy – titles, subtitles, and labels.
Alignment, whitespace, and the five-second test.
Conditional formatting and data bars for emphasis.

Module 9: Interactivity as a Storytelling Tool

Slicers, filters, and drill-through used to guide rather than overwhelm the viewer.
Bookmarks and buttons to build a guided, report-as-a-story experience.
Tooltips, including custom report-page tooltips, for layered detail.

Module 10: Communicating Findings to Stakeholders

Structuring an executive summary page.
Presenting a dashboard live: talking through insights rather than features.
Common pitfalls: too many KPIs on one page, mismatched chart types, misleading axes.

Day 2 workshop: Redesign an unpolished dashboard by applying storytelling and visual design principles, then present a before-and-after with the reasoning behind each change.

Day 3 – Advanced Dashboarding with Power BI

Module 11: Advanced DAX

Filter context deep dive: ALL, ALLEXCEPT, ALLSELECTED, REMOVEFILTERS.
Time intelligence: TOTALYTD, SAMEPERIODLASTYEAR, DATEADD, and custom calendar tables.
Iterators (SUMX, AVERAGEX, RANKX) and variables (VAR/RETURN) for readable, debuggable DAX.
SELECTEDVALUE and dynamic measure and title patterns.

Module 12: Advanced Data Modeling and Row-Level Security

Role-playing dimensions, such as Order Date vs. Ship Date.
Bridge tables for many-to-many relationships, and composite models.
Row-Level Security – static and dynamic – and testing it with View As Role.

Module 13: Performance Optimization

Using Performance Analyzer to find slow visuals and DAX.
Reducing model size: removing unused columns, disabling auto date and time.
Star-schema discipline and column cardinality as performance levers.

Module 14: Advanced Visualization Techniques

Field parameters and what-if parameters for dynamic dashboards.
Small multiples, custom visuals from AppSource, and drill-through pages.

Module 15: Deployment and Governance

Publishing to Power BI Service, workspaces, and apps.
Dataset refresh scheduling and gateways for on-premises sources.
Sharing, permissions, and an overview of deployment pipelines.

Day 3 workshop: Build a multi-page dashboard with role-level security, time-intelligence measures, drill-through, and at least one measured performance optimization, then publish it to a workspace.

Day 4 – Basic SQL for Power BI Developers and Capstone

Module 16: SQL Fundamentals

SELECT, FROM, WHERE, and ORDER BY.
Filtering with AND, OR, IN, BETWEEN, and LIKE.
Sorting and limiting results (TOP or LIMIT).

Module 17: Aggregation and Joins

GROUP BY, HAVING, and aggregate functions (COUNT, SUM, AVG, MIN, MAX).
INNER JOIN, LEFT JOIN, and RIGHT JOIN, with visual diagrams.
Mapping joins to Power BI relationships, joining fact and dimension tables.

Module 18: Writing Queries for Power BI

Using a SQL query directly in Get Data (native query) vs. letting Power Query build the SQL.
Understanding which Power Query steps fold to SQL vs. run locally.
Common Table Expressions (WITH) and SQL views for readability and simpler Power BI models.

Module 19: Applied Capstone and Presentation

Pull data using SQL, model it, and build advanced DAX measures.
Design a polished, story-driven dashboard applying visualization and interactivity principles.
Identify key findings, business implications, and recommended actions.
Deliver a concise presentation adapted to a management audience, with trainer and peer feedback.

Day 4 workshop: Given a normalized SQL database, write queries to answer five business questions, recreate the same answers as Power BI measures, then complete and present the capstone dashboard.

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Internal auditors and operational compliance personnel.
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Personnel responsible for reviewing warehouse controls and performance.

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Basic familiarity with warehouse, logistics, or inventory activities.
General understanding of business processes and operational documentation.
Basic numerical skills, including percentages and simple calculations.
Basic spreadsheet skills are helpful.
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Course Outline

Day 1: Warehouse Processes, Layout, and Storage Controls Module 1: Warehouse Operations and Process Controls

Warehouse roles within the supply chain.
End-to-end flow: receiving, put-away, storage, replenishment, picking, packing, and dispatch.
Physical stock movement and corresponding system transactions.
Responsibilities, authorization, and segregation of duties.
Common operational weaknesses and audit evidence.

Module 2: Warehouse Layout and Storage Systems

Functional zones, movement paths, and space utilization.
Receiving, storage, picking, staging, and dispatch areas.
Storage options: floor stacking, shelving, pallet racking, and bin storage.
Fixed and dynamic location systems.
Product slotting based on movement, size, handling needs, and compatibility.
Layout bottlenecks and their effects on productivity and inventory control.

Module 3: Receiving and Put-Away

Purchase orders, delivery documents, and expected receipts.
Quantity, condition, and item identification checks.
Recording shortages, overages, damage, and discrepancies.
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Put-away rules and location confirmation.
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Timely recording of receipts and stock movements.

Module 4: Storage, Stock Rotation, and Replenishment

Storage discipline and location integrity.
FIFO and FEFO principles, applications, and limitations.
Batch, lot, and expiry-date management.
Controls for slow-moving, obsolete, damaged, and expired stock.
Replenishment of picking locations.
Supervision and review of stock rotation compliance.

Day 2: Order Fulfillment and Inventory Accuracy Module 5: Picking Strategies and Order Fulfillment

Discrete, batch, zone, and wave picking.
Selecting picking strategies for different order profiles.
Pick paths, location sequencing, and travel reduction.
Replenishment coordination and stock availability.
Picking verification and error prevention.
Packing, staging, and dispatch confirmation.
Handling short picks, substitutions, cancellations, and returns.

Module 6: Inventory Records and Accuracy Controls

Item master data, units of measure, and location records.
On-hand, available, allocated, and quarantined stock.
Transaction controls for receipts, transfers, issues, returns, and adjustments.
Common causes of physical and system stock mismatches.
Barcode scanning and WMS control points.
Inventory reconciliation and supporting documentation.
Measuring inventory accuracy and interpreting discrepancies.

Module 7: Cycle Counting and Physical Inventory

Cycle counting versus full physical inventory.
ABC classification and risk-based count priorities.
Count frequency based on value, movement, and discrepancy history.
Count preparation, blind counting, and movement controls.
Recounts, variance investigation, and adjustment approval.
Root cause analysis and corrective action follow-up.
Reviewing the effectiveness of the counting program.

Module 8: Shrinkage Reduction and Inventory Protection

Sources of shrinkage: loss, damage, theft, misplacement, and recording errors.
Access controls and accountability for stock movements.
Controls for high-value and sensitive inventory.
Returns, write-offs, scrap, and disposal controls.
Exception reporting and unusual transaction patterns.
Investigating recurring losses and unexplained adjustments.
Preventive and detective controls for shrinkage reduction.

Day 3: Performance Management, Process Review, and Improvement Module 9: Warehouse Productivity and Inventory KPIs

Receiving productivity and dock-to-stock time.
Put-away productivity and replenishment performance.
Picking productivity and order accuracy.
Inventory accuracy and cycle count variance.
Space utilization and storage capacity.
Shrinkage, damage, and expiry indicators.
Defining KPI formulas, data sources, and reporting boundaries.
Interpreting trends while balancing speed, accuracy, and service.

Module 10: Supervising and Auditing Warehouse Processes

Process mapping and identification of critical control points.
Reviewing procedures against actual warehouse practices.
Tracing transactions from documents to systems and physical stock.
Reviewing approvals, access rights, and segregation of duties.
Identifying control gaps and recurring process exceptions.
Documenting findings with clear supporting evidence.
Prioritizing corrective actions and tracking closure.

Module 11: Warehouse Improvement Planning

Identifying bottlenecks, unnecessary movement, and rework.
Improving layout, slotting, and picking arrangements.
Addressing inventory discrepancy root causes.
Standardizing transaction recording and exception handling.
Assigning improvement owners and measurable targets.
Monitoring results and sustaining operational improvements.

Module 12: Capstone — Warehouse Operations and Inventory Improvement Plan

Review a warehouse scenario containing layout, transaction, inventory, and performance information.
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Recommend appropriate FIFO or FEFO controls.
Develop a cycle counting and inventory reconciliation plan.
Propose controls to reduce shrinkage and improve traceability.
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